Related Experiment Video
Updated: May 30, 2026
09:06
High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)
Published on: October 5, 2018
The ForeSee (4C) approach for integrative analysis in gene discovery
Yike Guo1, Robin E J Munro, Dimitrios Kalaitzopoulos
1Department of Computing, Imperial College London, London, UK. y.guo@imperial.ac.uk
Methods in Molecular Biology (Clifton, N.J.)
|July 23, 2011
Summary
This study introduces a workflow methodology for integrating diverse omics data, such as genomics and transcriptomics, to discover genes and biomarkers. The approach aids in identifying genomic alterations relevant to breast cancer research.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- High-throughput experimental techniques enable comprehensive cellular component measurements.
- Integrating and analyzing diverse biological data is crucial for modern biological research.
- Omics data analysis is essential for biomarker discovery and understanding complex diseases.
Purpose of the Study:
- To present a methodology for building integrative analytical workbenches using workflow technology.
- To focus on gene discovery through combined transcriptomics, genomics, and epigenomics data.
- To demonstrate the methodology's applicability in identifying aberrant genomic regions and genes for breast cancer research.
Main Methods:
- Utilizing workflow technology to create integrative analytical workbenches.
- Combining transcriptomics, genomics, and epigenomics data for gene discovery.
- Applying the methodology to identify genomic alterations and regulatory elements in breast cancer.
Main Results:
- Demonstrated a methodology for integrative omics data analysis.
- Identified aberrant genomic regions and genes with implications for breast cancer.
- Highlighted the potential of the methodology for biomarker discovery.
Conclusions:
- The presented workflow methodology facilitates integrative analysis of multi-omics data.
- This approach is valuable for gene discovery and biomarker identification, particularly in cancer research.
- Future work should address challenges and opportunities presented by next-generation sequencing technologies.
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